1. Field of the Invention
The invention disclosed and claimed herein generally pertains to a method and apparatus for linking together users of a social network with objects of a multimedia content network, in order to consider possible new relationships between the users and objects. More particularly, the invention pertains to a method of the above type that analyzes multiple linkages and relationships among the users and objects of the respective networks. Even more particularly, the invention pertains to a method of the above type that improves targeting of content delivery to users, and uses linkage analysis to enhance connections among users.
2. Description of the Related Art
Online communities are increasingly being formed into dynamic social networks that provide rich channels for interaction and communication among network users. There is great wealth of information contained within the relationships and links that make up these social networks. Moreover, global, distributed, multi-source multimedia (e.g., image, video and audio) information dissemination, such as through the Internet, wireless phones, and television, produces ever increasing amounts of content that is targeted to users. However, currently available solutions for connecting users to content rely mainly on matching user profiles or preferences to metadata that describes the content, or use recommendation systems such as collaborative filtering. Collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting taste information from many users (collaborating).
A drawback with the above currently available solutions is that they tend to require explicit creation of user profiles, or else they must have sufficient past history to effectively observe access patterns. Also, relying on a scoring or rating system that is averaged across an entire user base ignores the specific demands of the individual user. This approach is particularly deficient when used for tasks in which there is large variation in interest, such as making recommendations for movie titles or the like to a large and disparate user base.
Another emerging trend in social networks is crowd sourcing collaborative tagging of shared multimedia content. In collaborative tagging, metadata is generated by both the creators and the consumers of particular content, rather than by a single individual. The term metadata, as used herein, can include but is not limited to information pertaining to artifacts or other data, such as source, name, alt tag, attributes, the name of the collection to which the artifact belongs and/or its general purpose. The collaborative tagging approach has shown value, in supporting user access to online multimedia content through various recommendation systems in interactive social communities. Golder and Huberman, in “Usage Patterns of Collaborative Tagging Systems”, Journal of Information Science, Vol. 32 (2), 2006, showed that there are common social patterns to be discovered in collaborative tagging systems. As they analyzed a structure of these systems, they discovered regularities such as in user activity and tag frequencies. However, numerous technical problems still remain for effectively leveraging user communities from multimedia content enrichment, using collaborative tagging. These include dealing with ambiguity and synonymy in tags, and lack of vocabulary control.
Generally, the subject of achieving rich multimedia information linkage over multiple content sources has been largely ignored. With the growing variety of distribution channels for open source multimedia, the boundaries between different types of multimedia content domains are loosening. Nonetheless, existing content linking services tend to be of very narrow scope, and are typically based on associated metadata such as user, video title and related comments (e.g. YouTube, copyright © 2007 YouTube, Inc., or MySpace, © 2003-2007 MySpace.com.). As a result, information contained in the audio, visual and temporal dimensions of the multimedia content, and in their inherent semantic relationships, has not been significantly exploited. On the other hand, recent multimedia understanding research has produced significant results for automatic tagging of image, video and audio content, and using multimedia content analysis tools. For example, Tesic and Smith, in “Semantic Labeling of Multimedia Content Clusters”, IEEE International Conference on Multimedia and Expo (ICME), 2006, extended the scope of video summarization in a way that allows users to much more efficiently navigate the semantic and metadata space of the video data set.
It is anticipated that the relationships of a social network could be used to improve and enhance efforts to connect users to content networks and repositories. However, in the past little effort has been devoted to predicting important patterns and deriving relevant links in joint social and content space. Currently available techniques, including those discussed above, pertain to social and content links that are substantially limited to specific content and user domains. Such techniques include: (i) collaborative filtering, recommendation system and preference elicitation methods of connecting content to the users of social networks; (ii) social network analysis and collaborative portal invitations for user-to-user connections; and (iii) clustering and similarity searches in the visual space or associated metadata space (e.g., picture search based on tagging, file name and camera metadata), for content-to-content connections. However, in the present age of information explosion, each of these techniques, while useful, tends to be insufficient in view of the extremely large number of items that can now be found in even a single content category.